
Since its inception, zero-shot learning research has been dominated by the object classification problem [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}. Although it still remains as a challenging task, the zero-shot recognition has a number of limitations that render it unusable in real-life scenarios. First, it is destined to work for simpler cases where only a single dominant object is present in an image. Second, the attributes and semantic descriptions are relevant to individual objects instead of the entire scene composition. Third, zero-shot recognition provides an answer to unseen categories in elementary tasks, e.g., classification and retrieval, yet it is unable to scale to advanced tasks such as scene interpretation and contextual modeling, which require a fundamental reasoning about all salient objects in the scene. Fourth, global attributes are more susceptible to background variations, viewpoint, appearance and scale changes and practical factors such as occlusions and clutter. As a result, image-level ZSL fails for the case of complex scenes where a diverse set of competing attributes that do not belong to a single image-level category would exist.
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